In [206]:
%tensorflow_version 2.x
import tensorflow
tensorflow.__version__
Out[206]:
'2.3.0'
In [207]:
%matplotlib inline
import pandas as pd
import numpy as np
import tensorflow as tf
from google.colab import files
import seaborn as sns
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from sklearn import metrics
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, precision_recall_curve, auc
#uploaded = files.upload()
#uploaded = files.upload()

Pre-Processing Image Data

In [208]:
import numpy as np
Data = pd.read_csv("Labels.csv")
Data.shape
Out[208]:
(4750, 1)
In [209]:
img_array = np.load("images.npy", allow_pickle=True)
In [210]:
img_array.shape
Out[210]:
(4750, 128, 128, 3)
In [211]:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(10, 10, figsize=(100,100))
for i, ax in enumerate(axes.flat):
  ax.imshow(img_array[i])

Visualizing of Images

In [212]:
X_data = np.array(img_array[:,:,0,0])
In [213]:
X_data.shape
Out[213]:
(4750, 128)
In [214]:
y_data = Data
In [215]:
y_data.shape
Out[215]:
(4750, 1)
In [216]:
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3, random_state = 7)
In [217]:
from sklearn import preprocessing
X_train = preprocessing.normalize(X_train)
In [218]:
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(3325, 128)
(1425, 128)
(3325, 1)
(1425, 1)
In [219]:
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255
In [220]:
print("X_train shape:", X_train.shape)
print("Images in X_train:", X_train.shape[0])
print("Images in X_test:", X_test.shape[0])
print("Max value in X_train:", X_train.max())
print("Min value in X_train:", X_train.min())
X_train shape: (3325, 128)
Images in X_train: 3325
Images in X_test: 1425
Max value in X_train: 0.0014285049
Min value in X_train: 0.0
In [221]:
import cv2
from matplotlib import pyplot as plt
img_array = np.load("images.npy", allow_pickle=True)
fig, axes = plt.subplots(10, 10, figsize=(100,100))
for i, ax in enumerate(axes.flat):
  gaussian = cv2.GaussianBlur(img_array[i], (15, 15), 0)
  ax.imshow(gaussian)

Data Compatibility

In [222]:
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
y_train = enc.fit_transform(y_train)
y_test = enc.fit_transform(y_test)
print("Shape of y_train:", y_train.shape)
print("One value of y_train:", y_train[0])
Shape of y_train: (3325, 12)
One value of y_train: [0 0 0 1 0 0 0 0 0 0 0 0]
In [223]:
X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size = 0.5, random_state = 7)
In [224]:
validation_data = (X_validation, y_validation)
In [225]:
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
(3325, 128)
(712, 128)
(3325, 12)
(712, 12)
In [226]:
print(X_train[1])
[0.00057647 0.00054154 0.0005328  0.0005328  0.00051533 0.00048913
 0.00046293 0.00051533 0.0005328  0.00048913 0.00044546 0.00040178
 0.00041052 0.00042799 0.00042799 0.00040178 0.00039305 0.00034938
 0.00028824 0.00035811 0.00037558 0.00035811 0.00032317 0.00035811
 0.00036685 0.00037558 0.00038432 0.00038432 0.00039305 0.00036685
 0.00034938 0.00035811 0.00033191 0.00036685 0.00033191 0.00033191
 0.00029697 0.0002795  0.00027077 0.00026203 0.0002795  0.00028824
 0.00028824 0.00034938 0.00041052 0.00047166 0.00047166 0.00038432
 0.00019216 0.00020089 0.00030571 0.00032317 0.00030571 0.00032317
 0.00033191 0.00032317 0.00040178 0.00033191 0.00028824 0.00032317
 0.00032317 0.00032317 0.00031444 0.00031444 0.00031444 0.00030571
 0.00029697 0.00030571 0.00029697 0.00031444 0.00043672 0.00048039
 0.00037558 0.00033191 0.00031444 0.00030571 0.00028824 0.00028824
 0.0002533  0.00023583 0.00026203 0.0002795  0.00027077 0.0002271
 0.0002271  0.0002271  0.0002271  0.00023583 0.00023583 0.0002533
 0.0002533  0.00024456 0.0002271  0.00023583 0.0002795  0.00030571
 0.00035811 0.00039305 0.00035811 0.0002795  0.00034938 0.00029697
 0.00027077 0.00034938 0.00031444 0.00032317 0.00021836 0.00020963
 0.00026203 0.00032317 0.00031444 0.00028824 0.00033191 0.00035811
 0.00034938 0.00029697 0.0002533  0.00026203 0.00026203 0.00034064
 0.00038432 0.00038432 0.00039305 0.00041925 0.00033191 0.00028824
 0.00034064 0.00032317]
In [227]:
X_train = X_train.reshape((X_train.shape[0], 128)).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 128).astype('float32')

print(X_train.shape)
print(X_test.shape)
(3325, 128)
(712, 128)
In [228]:
X_train = np.expand_dims(X_train, axis = 2)
print(X_train.shape)
print(X_test.shape)
(3325, 128, 1)
(712, 128)

Building CNN

In [229]:
from tensorflow.keras import datasets, models, layers, optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from google.colab.patches import cv2_imshow
In [230]:
# Set the CNN model

batch_size = None

model = models.Sequential()
model.add(layers.Conv2D(32, (5, 5), padding='same', activation="relu", input_shape=(128,128,1),data_format='channels_first'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))
model.add(layers.Conv2D(64, (5, 5), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation="relu"))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.5))

model.add(layers.GlobalMaxPooling2D())
model.add(layers.Dense(256, activation="relu"))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation="softmax"))

model.summary()
Model: "sequential_18"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_20 (Conv2D)           (None, 32, 128, 1)        102432    
_________________________________________________________________
batch_normalization_20 (Batc (None, 32, 128, 1)        4         
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 16, 64, 1)         0         
_________________________________________________________________
dropout_25 (Dropout)         (None, 16, 64, 1)         0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 16, 64, 64)        1664      
_________________________________________________________________
batch_normalization_21 (Batc (None, 16, 64, 64)        256       
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 8, 32, 64)         0         
_________________________________________________________________
dropout_26 (Dropout)         (None, 8, 32, 64)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 8, 32, 64)         36928     
_________________________________________________________________
batch_normalization_22 (Batc (None, 8, 32, 64)         256       
_________________________________________________________________
max_pooling2d_22 (MaxPooling (None, 4, 16, 64)         0         
_________________________________________________________________
dropout_27 (Dropout)         (None, 4, 16, 64)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 4, 16, 64)         36928     
_________________________________________________________________
batch_normalization_23 (Batc (None, 4, 16, 64)         256       
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 2, 8, 64)          0         
_________________________________________________________________
dropout_28 (Dropout)         (None, 2, 8, 64)          0         
_________________________________________________________________
global_max_pooling2d_5 (Glob (None, 64)                0         
_________________________________________________________________
dense_33 (Dense)             (None, 256)               16640     
_________________________________________________________________
dropout_29 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_34 (Dense)             (None, 10)                2570      
=================================================================
Total params: 197,934
Trainable params: 197,548
Non-trainable params: 386
_________________________________________________________________
In [231]:
opt = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
In [232]:
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])
In [233]:
model.summary()
Model: "sequential_18"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_20 (Conv2D)           (None, 32, 128, 1)        102432    
_________________________________________________________________
batch_normalization_20 (Batc (None, 32, 128, 1)        4         
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 16, 64, 1)         0         
_________________________________________________________________
dropout_25 (Dropout)         (None, 16, 64, 1)         0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 16, 64, 64)        1664      
_________________________________________________________________
batch_normalization_21 (Batc (None, 16, 64, 64)        256       
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 8, 32, 64)         0         
_________________________________________________________________
dropout_26 (Dropout)         (None, 8, 32, 64)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 8, 32, 64)         36928     
_________________________________________________________________
batch_normalization_22 (Batc (None, 8, 32, 64)         256       
_________________________________________________________________
max_pooling2d_22 (MaxPooling (None, 4, 16, 64)         0         
_________________________________________________________________
dropout_27 (Dropout)         (None, 4, 16, 64)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 4, 16, 64)         36928     
_________________________________________________________________
batch_normalization_23 (Batc (None, 4, 16, 64)         256       
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 2, 8, 64)          0         
_________________________________________________________________
dropout_28 (Dropout)         (None, 2, 8, 64)          0         
_________________________________________________________________
global_max_pooling2d_5 (Glob (None, 64)                0         
_________________________________________________________________
dense_33 (Dense)             (None, 256)               16640     
_________________________________________________________________
dropout_29 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_34 (Dense)             (None, 10)                2570      
=================================================================
Total params: 197,934
Trainable params: 197,548
Non-trainable params: 386
_________________________________________________________________

Evaluate the model.

Hi, last night, my model.fit function stopped working all of a sudden. I am not able to make even the mentor session examples work. In order for my code to compile, I am using a really simple model. This is my original code: model1.fit( x = X_train, y=y_train, batch_size=128, epochs=10, validation_split = 0.5). It worked up until some point, and then it is not. I tried classroom examples too and am getting errors. I can't make this code work with the simplified model: scores = model2.evaluate(x, y, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]).

In [234]:
model2 = Sequential()
model2.add(Dense(1, input_shape=(1,)))
model2.compile(loss='mse', optimizer='rmsprop')

# The fit() method - trains the model
x = np.random.uniform(0.0, 1.0, (200))
y = 0.3 + 0.6*x + np.random.normal(0.0, 0.05,(200))
model2.fit(x, y, epochs=1000, batch_size=100)
Epoch 1/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.8575
Epoch 2/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.8445
Epoch 3/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.8353
Epoch 4/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.8275
Epoch 5/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.8204
Epoch 6/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.8139
Epoch 7/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.8077
Epoch 8/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.8017
Epoch 9/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7960
Epoch 10/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7903
Epoch 11/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.7848
Epoch 12/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7794
Epoch 13/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7741
Epoch 14/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7688
Epoch 15/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7636
Epoch 16/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7584
Epoch 17/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7533
Epoch 18/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7482
Epoch 19/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7432
Epoch 20/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7381
Epoch 21/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7332
Epoch 22/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7282
Epoch 23/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7232
Epoch 24/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7183
Epoch 25/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.7134
Epoch 26/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7085
Epoch 27/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.7036
Epoch 28/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6988
Epoch 29/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6939
Epoch 30/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6891
Epoch 31/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6844
Epoch 32/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.6796
Epoch 33/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6748
Epoch 34/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6701
Epoch 35/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6654
Epoch 36/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6607
Epoch 37/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6560
Epoch 38/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6514
Epoch 39/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6468
Epoch 40/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6421
Epoch 41/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6375
Epoch 42/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6330
Epoch 43/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.6284
Epoch 44/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6238
Epoch 45/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6193
Epoch 46/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6148
Epoch 47/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6103
Epoch 48/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6058
Epoch 49/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.6013
Epoch 50/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5969
Epoch 51/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5925
Epoch 52/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5881
Epoch 53/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5837
Epoch 54/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5793
Epoch 55/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.5749
Epoch 56/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5706
Epoch 57/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5663
Epoch 58/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5620
Epoch 59/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.5577
Epoch 60/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5534
Epoch 61/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.5492
Epoch 62/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5450
Epoch 63/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.5407
Epoch 64/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5365
Epoch 65/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.5324
Epoch 66/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5282
Epoch 67/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.5241
Epoch 68/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5199
Epoch 69/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5158
Epoch 70/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5117
Epoch 71/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.5077
Epoch 72/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.5036
Epoch 73/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4996
Epoch 74/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4956
Epoch 75/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4916
Epoch 76/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4876
Epoch 77/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4836
Epoch 78/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4797
Epoch 79/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4757
Epoch 80/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4718
Epoch 81/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4679
Epoch 82/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4641
Epoch 83/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4602
Epoch 84/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4563
Epoch 85/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4525
Epoch 86/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4487
Epoch 87/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4449
Epoch 88/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4411
Epoch 89/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4374
Epoch 90/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4336
Epoch 91/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4299
Epoch 92/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4262
Epoch 93/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4225
Epoch 94/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4189
Epoch 95/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4152
Epoch 96/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4116
Epoch 97/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4080
Epoch 98/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.4044
Epoch 99/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.4008
Epoch 100/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3973
Epoch 101/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3937
Epoch 102/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3902
Epoch 103/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3867
Epoch 104/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3832
Epoch 105/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3797
Epoch 106/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3763
Epoch 107/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3728
Epoch 108/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3694
Epoch 109/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3660
Epoch 110/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3626
Epoch 111/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3593
Epoch 112/1000
2/2 [==============================] - 0s 5ms/step - loss: 0.3559
Epoch 113/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3526
Epoch 114/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3493
Epoch 115/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3460
Epoch 116/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3427
Epoch 117/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3395
Epoch 118/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3362
Epoch 119/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3330
Epoch 120/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3298
Epoch 121/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3266
Epoch 122/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3234
Epoch 123/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3202
Epoch 124/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3171
Epoch 125/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.3140
Epoch 126/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3109
Epoch 127/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3078
Epoch 128/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3047
Epoch 129/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.3017
Epoch 130/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2987
Epoch 131/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2956
Epoch 132/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2927
Epoch 133/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2897
Epoch 134/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2867
Epoch 135/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2838
Epoch 136/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2808
Epoch 137/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2779
Epoch 138/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2750
Epoch 139/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2722
Epoch 140/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2693
Epoch 141/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2665
Epoch 142/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2637
Epoch 143/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2609
Epoch 144/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2581
Epoch 145/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2553
Epoch 146/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2525
Epoch 147/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2498
Epoch 148/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2471
Epoch 149/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2444
Epoch 150/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2417
Epoch 151/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2390
Epoch 152/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2364
Epoch 153/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.2338
Epoch 154/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2311
Epoch 155/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.2285
Epoch 156/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2260
Epoch 157/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2234
Epoch 158/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2209
Epoch 159/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2184
Epoch 160/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2159
Epoch 161/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2134
Epoch 162/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2109
Epoch 163/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.2084
Epoch 164/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.2060
Epoch 165/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2036
Epoch 166/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.2012
Epoch 167/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1988
Epoch 168/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1964
Epoch 169/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1941
Epoch 170/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1917
Epoch 171/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1894
Epoch 172/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.1871
Epoch 173/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1848
Epoch 174/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1826
Epoch 175/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1803
Epoch 176/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1781
Epoch 177/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1759
Epoch 178/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1737
Epoch 179/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1715
Epoch 180/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1694
Epoch 181/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1672
Epoch 182/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1651
Epoch 183/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1630
Epoch 184/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1609
Epoch 185/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1588
Epoch 186/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1568
Epoch 187/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1547
Epoch 188/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1527
Epoch 189/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1507
Epoch 190/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1487
Epoch 191/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1468
Epoch 192/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1448
Epoch 193/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1429
Epoch 194/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1410
Epoch 195/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1391
Epoch 196/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1372
Epoch 197/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1353
Epoch 198/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1334
Epoch 199/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1316
Epoch 200/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1298
Epoch 201/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1280
Epoch 202/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1262
Epoch 203/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1245
Epoch 204/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1227
Epoch 205/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1210
Epoch 206/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1193
Epoch 207/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1176
Epoch 208/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1159
Epoch 209/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1143
Epoch 210/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1126
Epoch 211/1000
2/2 [==============================] - 0s 5ms/step - loss: 0.1110
Epoch 212/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1094
Epoch 213/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1078
Epoch 214/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1062
Epoch 215/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.1047
Epoch 216/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1031
Epoch 217/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1016
Epoch 218/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.1001
Epoch 219/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0986
Epoch 220/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0972
Epoch 221/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0957
Epoch 222/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0943
Epoch 223/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0929
Epoch 224/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0915
Epoch 225/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0901
Epoch 226/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0887
Epoch 227/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0873
Epoch 228/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0860
Epoch 229/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0847
Epoch 230/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0834
Epoch 231/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0821
Epoch 232/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0809
Epoch 233/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0796
Epoch 234/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0784
Epoch 235/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0772
Epoch 236/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0760
Epoch 237/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0748
Epoch 238/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0736
Epoch 239/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0725
Epoch 240/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0714
Epoch 241/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0702
Epoch 242/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0691
Epoch 243/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0681
Epoch 244/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0670
Epoch 245/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0660
Epoch 246/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0649
Epoch 247/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0639
Epoch 248/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0629
Epoch 249/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0619
Epoch 250/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0610
Epoch 251/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0600
Epoch 252/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0591
Epoch 253/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0582
Epoch 254/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0573
Epoch 255/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0564
Epoch 256/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0555
Epoch 257/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0546
Epoch 258/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0538
Epoch 259/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0530
Epoch 260/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0522
Epoch 261/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0514
Epoch 262/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0506
Epoch 263/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0499
Epoch 264/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0491
Epoch 265/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0484
Epoch 266/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0477
Epoch 267/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0470
Epoch 268/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0464
Epoch 269/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0457
Epoch 270/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0451
Epoch 271/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0444
Epoch 272/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0438
Epoch 273/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0432
Epoch 274/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0427
Epoch 275/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0421
Epoch 276/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0415
Epoch 277/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0410
Epoch 278/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0405
Epoch 279/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0400
Epoch 280/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0395
Epoch 281/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0390
Epoch 282/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0386
Epoch 283/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0381
Epoch 284/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0377
Epoch 285/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0373
Epoch 286/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0369
Epoch 287/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0365
Epoch 288/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0361
Epoch 289/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0358
Epoch 290/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0354
Epoch 291/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0351
Epoch 292/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0348
Epoch 293/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0345
Epoch 294/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0342
Epoch 295/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0339
Epoch 296/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0336
Epoch 297/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0333
Epoch 298/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0331
Epoch 299/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0328
Epoch 300/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0326
Epoch 301/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0324
Epoch 302/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0322
Epoch 303/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0320
Epoch 304/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0318
Epoch 305/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0316
Epoch 306/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0314
Epoch 307/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0312
Epoch 308/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0310
Epoch 309/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0308
Epoch 310/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0307
Epoch 311/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0305
Epoch 312/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0303
Epoch 313/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0302
Epoch 314/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0300
Epoch 315/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0298
Epoch 316/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0296
Epoch 317/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0295
Epoch 318/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0293
Epoch 319/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0291
Epoch 320/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0289
Epoch 321/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0287
Epoch 322/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0285
Epoch 323/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0284
Epoch 324/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0282
Epoch 325/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0280
Epoch 326/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0279
Epoch 327/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0277
Epoch 328/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0275
Epoch 329/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0274
Epoch 330/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0271
Epoch 331/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0270
Epoch 332/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0268
Epoch 333/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0266
Epoch 334/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0265
Epoch 335/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0263
Epoch 336/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0262
Epoch 337/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0260
Epoch 338/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0258
Epoch 339/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0257
Epoch 340/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0255
Epoch 341/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0253
Epoch 342/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0252
Epoch 343/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0250
Epoch 344/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0248
Epoch 345/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0246
Epoch 346/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0245
Epoch 347/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0243
Epoch 348/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0241
Epoch 349/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0240
Epoch 350/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0238
Epoch 351/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0236
Epoch 352/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0235
Epoch 353/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0233
Epoch 354/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0232
Epoch 355/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0230
Epoch 356/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0229
Epoch 357/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0227
Epoch 358/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0225
Epoch 359/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0223
Epoch 360/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0222
Epoch 361/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0221
Epoch 362/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0219
Epoch 363/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0217
Epoch 364/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0216
Epoch 365/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0214
Epoch 366/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0213
Epoch 367/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0211
Epoch 368/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0210
Epoch 369/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0208
Epoch 370/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0207
Epoch 371/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0205
Epoch 372/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0204
Epoch 373/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0202
Epoch 374/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0200
Epoch 375/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0199
Epoch 376/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0198
Epoch 377/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0196
Epoch 378/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0195
Epoch 379/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0193
Epoch 380/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0192
Epoch 381/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0190
Epoch 382/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0189
Epoch 383/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0187
Epoch 384/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0186
Epoch 385/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0184
Epoch 386/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0183
Epoch 387/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0182
Epoch 388/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0180
Epoch 389/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0179
Epoch 390/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0177
Epoch 391/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0176
Epoch 392/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0175
Epoch 393/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0173
Epoch 394/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0172
Epoch 395/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0170
Epoch 396/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0169
Epoch 397/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0168
Epoch 398/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0166
Epoch 399/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0165
Epoch 400/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0164
Epoch 401/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0162
Epoch 402/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0161
Epoch 403/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0160
Epoch 404/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0158
Epoch 405/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0157
Epoch 406/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0156
Epoch 407/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0154
Epoch 408/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0153
Epoch 409/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0152
Epoch 410/1000
2/2 [==============================] - 0s 8ms/step - loss: 0.0151
Epoch 411/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0149
Epoch 412/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0148
Epoch 413/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0147
Epoch 414/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0146
Epoch 415/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0144
Epoch 416/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0143
Epoch 417/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0142
Epoch 418/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0141
Epoch 419/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0140
Epoch 420/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0138
Epoch 421/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0137
Epoch 422/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0136
Epoch 423/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0135
Epoch 424/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0134
Epoch 425/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0132
Epoch 426/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0131
Epoch 427/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0130
Epoch 428/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0129
Epoch 429/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0128
Epoch 430/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0127
Epoch 431/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0126
Epoch 432/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0124
Epoch 433/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0123
Epoch 434/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0122
Epoch 435/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0121
Epoch 436/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0120
Epoch 437/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0119
Epoch 438/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0118
Epoch 439/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0117
Epoch 440/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0116
Epoch 441/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0115
Epoch 442/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0114
Epoch 443/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0113
Epoch 444/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0112
Epoch 445/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0111
Epoch 446/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0110
Epoch 447/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0109
Epoch 448/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0107
Epoch 449/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0106
Epoch 450/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0105
Epoch 451/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0104
Epoch 452/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0103
Epoch 453/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0102
Epoch 454/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0101
Epoch 455/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0100
Epoch 456/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0100
Epoch 457/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0098
Epoch 458/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0098
Epoch 459/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0097
Epoch 460/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0096
Epoch 461/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0095
Epoch 462/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0094
Epoch 463/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0093
Epoch 464/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0092
Epoch 465/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0091
Epoch 466/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0090
Epoch 467/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0089
Epoch 468/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0088
Epoch 469/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0088
Epoch 470/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0087
Epoch 471/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0086
Epoch 472/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0085
Epoch 473/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0084
Epoch 474/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0083
Epoch 475/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0082
Epoch 476/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0081
Epoch 477/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0081
Epoch 478/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0080
Epoch 479/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0079
Epoch 480/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0078
Epoch 481/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0077
Epoch 482/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0076
Epoch 483/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0076
Epoch 484/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0075
Epoch 485/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0074
Epoch 486/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0073
Epoch 487/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0073
Epoch 488/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0072
Epoch 489/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0071
Epoch 490/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0070
Epoch 491/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0070
Epoch 492/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0069
Epoch 493/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0068
Epoch 494/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0067
Epoch 495/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0067
Epoch 496/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0066
Epoch 497/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0065
Epoch 498/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0065
Epoch 499/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0064
Epoch 500/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0063
Epoch 501/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0062
Epoch 502/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0062
Epoch 503/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0061
Epoch 504/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0060
Epoch 505/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0060
Epoch 506/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0059
Epoch 507/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0059
Epoch 508/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0058
Epoch 509/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0057
Epoch 510/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0057
Epoch 511/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0056
Epoch 512/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0055
Epoch 513/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0055
Epoch 514/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0054
Epoch 515/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0054
Epoch 516/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0053
Epoch 517/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0052
Epoch 518/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0052
Epoch 519/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0051
Epoch 520/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0051
Epoch 521/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0050
Epoch 522/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0050
Epoch 523/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0049
Epoch 524/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0049
Epoch 525/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0048
Epoch 526/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0047
Epoch 527/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0047
Epoch 528/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0047
Epoch 529/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0046
Epoch 530/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0045
Epoch 531/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0045
Epoch 532/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0044
Epoch 533/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0044
Epoch 534/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0044
Epoch 535/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0043
Epoch 536/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0043
Epoch 537/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0042
Epoch 538/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0042
Epoch 539/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0041
Epoch 540/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0041
Epoch 541/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0040
Epoch 542/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0040
Epoch 543/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0039
Epoch 544/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 545/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 546/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 547/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 548/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 549/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 550/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 551/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 552/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 553/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 554/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0035
Epoch 555/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 556/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 557/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 558/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 559/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 560/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0033
Epoch 561/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0033
Epoch 562/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 563/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 564/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 565/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 566/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0032
Epoch 567/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 568/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 569/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 570/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 571/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0030
Epoch 572/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0030
Epoch 573/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 574/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 575/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 576/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 577/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 578/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 579/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0028
Epoch 580/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 581/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 582/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 583/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0028
Epoch 584/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 585/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 586/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 587/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 588/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 589/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 590/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 591/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 592/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0026
Epoch 593/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 594/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 595/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 596/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 597/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 598/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 599/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 600/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 601/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 602/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 603/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 604/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 605/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 606/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 607/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 608/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 609/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 610/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 611/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 612/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 613/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 614/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 615/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 616/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0025
Epoch 617/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 618/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 619/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 620/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 621/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 622/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 623/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 624/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 625/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 626/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 627/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 628/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 629/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 630/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 631/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 632/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 633/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 634/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 635/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 636/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 637/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 638/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 639/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 640/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 641/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 642/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 643/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 644/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 645/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 646/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 647/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 648/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 649/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 650/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 651/1000
2/2 [==============================] - 0s 7ms/step - loss: 0.0024
Epoch 652/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 653/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 654/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 655/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 656/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 657/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 658/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 659/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 660/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 661/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 662/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 663/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 664/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 665/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 666/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 667/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 668/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 669/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 670/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 671/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 672/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 673/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 674/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 675/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 676/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 677/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 678/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 679/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 680/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 681/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 682/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 683/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 684/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 685/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 686/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 687/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 688/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 689/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 690/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 691/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 692/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 693/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 694/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 695/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 696/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 697/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 698/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 699/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 700/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 701/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 702/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 703/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 704/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 705/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 706/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 707/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 708/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 709/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 710/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 711/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 712/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 713/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 714/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 715/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 716/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 717/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 718/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 719/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 720/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 721/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 722/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 723/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 724/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 725/1000
2/2 [==============================] - 0s 7ms/step - loss: 0.0024
Epoch 726/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 727/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 728/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 729/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 730/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 731/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 732/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 733/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 734/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 735/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 736/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 737/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 738/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 739/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 740/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 741/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 742/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 743/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 744/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 745/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 746/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 747/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 748/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 749/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 750/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 751/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 752/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 753/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 754/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 755/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 756/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 757/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 758/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 759/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 760/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 761/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 762/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 763/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 764/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 765/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 766/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 767/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 768/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 769/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 770/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 771/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 772/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 773/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 774/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 775/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 776/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 777/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 778/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 779/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 780/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 781/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 782/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 783/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 784/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 785/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 786/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 787/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 788/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 789/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 790/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 791/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 792/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 793/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 794/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 795/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 796/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 797/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 798/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 799/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 800/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 801/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 802/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 803/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 804/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 805/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 806/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 807/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 808/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 809/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 810/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 811/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 812/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 813/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 814/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 815/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 816/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 817/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 818/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 819/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 820/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 821/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 822/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 823/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 824/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 825/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 826/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 827/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 828/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 829/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 830/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 831/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 832/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 833/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 834/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 835/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 836/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 837/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 838/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 839/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 840/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 841/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 842/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 843/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 844/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 845/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 846/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 847/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 848/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 849/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 850/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 851/1000
2/2 [==============================] - 0s 5ms/step - loss: 0.0024
Epoch 852/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 853/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 854/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 855/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 856/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 857/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 858/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 859/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 860/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 861/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 862/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 863/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 864/1000
2/2 [==============================] - 0s 5ms/step - loss: 0.0024
Epoch 865/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 866/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 867/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 868/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 869/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 870/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 871/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 872/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 873/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 874/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 875/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 876/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 877/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 878/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 879/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 880/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 881/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 882/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 883/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 884/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 885/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 886/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 887/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 888/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 889/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 890/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 891/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 892/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 893/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 894/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 895/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 896/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 897/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 898/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 899/1000
2/2 [==============================] - 0s 7ms/step - loss: 0.0024
Epoch 900/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 901/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 902/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 903/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 904/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 905/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 906/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 907/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 908/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 909/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 910/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 911/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 912/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 913/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 914/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 915/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 916/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 917/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 918/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 919/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 920/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 921/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 922/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 923/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 924/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 925/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 926/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 927/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 928/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 929/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 930/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 931/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 932/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 933/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 934/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 935/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 936/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 937/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 938/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 939/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 940/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 941/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 942/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 943/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 944/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 945/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 946/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 947/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 948/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 949/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 950/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 951/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 952/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 953/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 954/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 955/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 956/1000
2/2 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 957/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 958/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 959/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 960/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 961/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 962/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 963/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 964/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 965/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 966/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 967/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 968/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 969/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 970/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 971/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 972/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 973/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 974/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 975/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 976/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 977/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 978/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 979/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 980/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 981/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 982/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 983/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 984/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 985/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 986/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 987/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 988/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 989/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 990/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 991/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 992/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 993/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 994/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 995/1000
2/2 [==============================] - 0s 4ms/step - loss: 0.0024
Epoch 996/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 997/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 998/1000
2/2 [==============================] - 0s 3ms/step - loss: 0.0024
Epoch 999/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1000/1000
2/2 [==============================] - 0s 2ms/step - loss: 0.0024
Out[234]:
<tensorflow.python.keras.callbacks.History at 0x7f35f52fc320>
In [235]:
results = model2.evaluate(x, y)
Y_pred_cls = model2.predict_classes(y, batch_size=200, verbose=0)
print('Accuracy Model (Dropout): '+ str(model2.evaluate(x,y)))
print('Recall_score: ' + str(recall_score(Y_pred_cls, Y_pred_cls)))
print('Precision_score: ' + str(precision_score(Y_pred_cls, Y_pred_cls)))
print('F-score: ' + str(f1_score(Y_pred_cls,Y_pred_cls)))
conf = confusion_matrix(Y_pred_cls, Y_pred_cls)
sns.heatmap(conf.T, square=True, annot=True, cbar=False, cmap=plt.cm.Blues)
plt.xlabel('Predicted Values')
plt.ylabel('True Values');
plt.show();
7/7 [==============================] - 0s 1ms/step - loss: 0.0024
7/7 [==============================] - 0s 1ms/step - loss: 0.0024
Accuracy Model (Dropout): 0.0024116404820233583
Recall_score: 1.0
Precision_score: 1.0
F-score: 1.0
In [236]:
from keras.utils.vis_utils import plot_model
plot_model(model2, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
Out[236]:
In [237]:
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.9, random_state=0)
y_pred = model2.predict(X_test)
import numpy as np
from matplotlib import pyplot as plt

data = np.array([
    [X_test[2], y_pred[2]],
    [X_test[3], y_pred[3]],
    [X_test[33], y_pred[33]],
    [X_test[36], y_pred[36]],
    [X_test[59], y_pred[59]],
])
x, y = data.T
plt.scatter(x,y)
plt.show()